Samantha 7B needs ~14.0 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~60 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
60.4 tok/s
TTFT
3204 ms
Safe context
4K
Memory
14.0 GB / 46.1 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 60.4 tok/s | 1748 ms | 4K |
| Coding | B | Runs well | 60.4 tok/s | 3204 ms | 4K |
| Agentic Coding | B | Runs well | 60.4 tok/s | 4661 ms | 4K |
| Reasoning | B | Runs well | 60.4 tok/s | 3787 ms | 4K |
| RAG | B | Runs well | 60.4 tok/s | 5826 ms | 4K |
How Samantha 7B (7B params) fits at each quantization level on MacBook Pro M3 Max 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B58 |
Q3_K_S | 3 | 3.4 GB | Low | B58 |
NVFP4 | 4 |
Copy-paste commands to run Samantha 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "cognitivecomputations/samantha-1.1-llama-7b" \
--hf-file "samantha-1.1-llama-7b-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Raises estimated decode speed by about 56%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 62%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M3 Max 64GB can run Samantha 7B with a B grade (Runs well). Expected decode speed: 60.4 tok/s.
Samantha 7B (7B parameters) requires approximately 14.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Samantha 7B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Max 64GB, Samantha 7B achieves approximately 60.4 tokens per second decode speed with a time-to-first-token of 3204ms using Q4_K_M quantization.
For coding workloads, Samantha 7B on MacBook Pro M3 Max 64GB receives a B grade with 60.4 tok/s and 4K context.
On MacBook Pro M3 Max 64GB, Samantha 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/samantha-7b-on-m3-max-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| B58 |
Q4_K_M | 4 | 4.3 GB | Medium | B58 |
Q5_K_M | 5 | 5.0 GB | High | B58 |
Q6_K | 6 | 5.7 GB | High | B58 |
Q8_0 | 8 | 7.5 GB | Very High | B59 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B60 |
Not always. MacBook Pro M3 Max 64GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.